format(table.get("title", "")))
# Transform the data
- data = input_data.filter_data(table)
+ data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
try:
format(table.get("title", "")))
# Transform the data
- data = input_data.filter_data(table)
+ data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
try:
item.append(round(stdev(data_t) / 1000000, 2))
else:
item.extend([None, None])
- if item[1] is not None and item[3] is not None:
+ if item[1] is not None and item[3] is not None and item[1] != 0:
item.append(int(relative_change(float(item[1]), float(item[3]))))
if len(item) == 6:
tbl_lst.append(item)
format(table.get("title", "")))
# Transform the data
- data = input_data.filter_data(table)
+ data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
header = ["Test case",
- "Thput trend [Mpps]",
- "Anomaly [Mpps]",
+ "Throughput Trend [Mpps]",
+ "Trend Compliance",
+ "Anomaly Value [Mpps]",
"Change [%]",
- "Classification"]
+ "#Outliers"
+ ]
header_str = ",".join(header) + "\n"
# Prepare data to the table:
"-".join(tst_data["name"].
split("-")[1:]))
tbl_dict[tst_name] = {"name": name,
- "data": list()}
+ "data": dict()}
try:
- tbl_dict[tst_name]["data"]. \
- append(tst_data["result"]["throughput"])
+ tbl_dict[tst_name]["data"][str(build)] = \
+ tst_data["result"]["throughput"]
except (TypeError, KeyError):
pass # No data in output.xml for this test
tbl_lst = list()
for tst_name in tbl_dict.keys():
if len(tbl_dict[tst_name]["data"]) > 2:
- sample_lst = tbl_dict[tst_name]["data"]
- pd_data = pd.Series(sample_lst)
+
+ pd_data = pd.Series(tbl_dict[tst_name]["data"])
win_size = pd_data.size \
if pd_data.size < table["window"] else table["window"]
# Test name:
name = tbl_dict[tst_name]["name"]
- # Trend list:
- trend_lst = list(pd_data.rolling(window=win_size, min_periods=2).
- median())
- # Stdevs list:
- t_data, _ = find_outliers(pd_data)
- t_data_lst = list(t_data)
- stdev_lst = list(t_data.rolling(window=win_size, min_periods=2).
- std())
+ median = pd_data.rolling(window=win_size, min_periods=2).median()
+ trimmed_data, _ = find_outliers(pd_data, outlier_const=1.5)
+ stdev_t = pd_data.rolling(window=win_size, min_periods=2).std()
rel_change_lst = [None, ]
classification_lst = [None, ]
- for idx in range(1, len(trend_lst)):
+ median_lst = [None, ]
+ sample_lst = [None, ]
+ first = True
+ for build_nr, value in pd_data.iteritems():
+ if first:
+ first = False
+ continue
# Relative changes list:
- if not isnan(sample_lst[idx]) \
- and not isnan(trend_lst[idx])\
- and trend_lst[idx] != 0:
+ if not isnan(value) \
+ and not isnan(median[build_nr]) \
+ and median[build_nr] != 0:
rel_change_lst.append(
- int(relative_change(float(trend_lst[idx]),
- float(sample_lst[idx]))))
+ int(relative_change(float(median[build_nr]),
+ float(value))))
else:
rel_change_lst.append(None)
+
# Classification list:
- if isnan(t_data_lst[idx]) or isnan(stdev_lst[idx]):
+ if isnan(trimmed_data[build_nr]) \
+ or isnan(median[build_nr]) \
+ or isnan(stdev_t[build_nr]) \
+ or isnan(value):
classification_lst.append("outlier")
- elif sample_lst[idx] < (trend_lst[idx] - 3*stdev_lst[idx]):
+ elif value < (median[build_nr] - 3 * stdev_t[build_nr]):
classification_lst.append("regression")
- elif sample_lst[idx] > (trend_lst[idx] + 3*stdev_lst[idx]):
+ elif value > (median[build_nr] + 3 * stdev_t[build_nr]):
classification_lst.append("progression")
else:
classification_lst.append("normal")
+ sample_lst.append(value)
+ median_lst.append(median[build_nr])
- last_idx = len(sample_lst) - 1
+ last_idx = len(classification_lst) - 1
first_idx = last_idx - int(table["evaluated-window"])
if first_idx < 0:
first_idx = 0
classification = "outlier"
elif "progression" in classification_lst[first_idx:]:
classification = "progression"
- else:
+ elif "normal" in classification_lst[first_idx:]:
classification = "normal"
+ else:
+ classification = None
+
+ nr_outliers = 0
+ consecutive_outliers = 0
+ failure = False
+ for item in classification_lst[first_idx:]:
+ if item == "outlier":
+ nr_outliers += 1
+ consecutive_outliers += 1
+ if consecutive_outliers == 3:
+ failure = True
+ else:
+ consecutive_outliers = 0
idx = len(classification_lst) - 1
while idx:
break
idx -= 1
- trend = round(float(trend_lst[-2]) / 1000000, 2) \
- if not isnan(trend_lst[-2]) else ''
+ if failure:
+ classification = "failure"
+ elif classification == "outlier":
+ classification = "normal"
+
+ trend = round(float(median_lst[-1]) / 1000000, 2) \
+ if not isnan(median_lst[-1]) else ''
sample = round(float(sample_lst[idx]) / 1000000, 2) \
if not isnan(sample_lst[idx]) else ''
rel_change = rel_change_lst[idx] \
if rel_change_lst[idx] is not None else ''
tbl_lst.append([name,
trend,
- sample,
- rel_change,
- classification])
+ classification,
+ '-' if classification == "normal" else sample,
+ '-' if classification == "normal" else rel_change,
+ nr_outliers])
# Sort the table according to the classification
tbl_sorted = list()
- for classification in ("regression", "outlier", "progression", "normal"):
- tbl_tmp = [item for item in tbl_lst if item[4] == classification]
+ for classification in ("failure", "regression", "progression", "normal"):
+ tbl_tmp = [item for item in tbl_lst if item[2] == classification]
tbl_tmp.sort(key=lambda rel: rel[0])
tbl_sorted.extend(tbl_tmp)
# Table header:
tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor="#6699ff"))
for idx, item in enumerate(csv_lst[0]):
- alignment = "left" if idx == 0 else "right"
+ alignment = "left" if idx == 0 else "center"
th = ET.SubElement(tr, "th", attrib=dict(align=alignment))
th.text = item
for c_idx, item in enumerate(row):
alignment = "left" if c_idx == 0 else "center"
td = ET.SubElement(tr, "td", attrib=dict(align=alignment))
- if c_idx == 4:
+ if c_idx == 2:
if item == "regression":
td.set("bgcolor", "#eca1a6")
- elif item == "outlier":
+ elif item == "failure":
td.set("bgcolor", "#d6cbd3")
elif item == "progression":
td.set("bgcolor", "#bdcebe")
Code Review / csit.git / blobdiff